Wire windage yaw monitoring method and system fusing angle and distance measurement information

Abstract

The invention provides a wire windage yaw monitoring method and system fusing angle and distance measurement information, and the method comprises the steps: collecting angles for multiple times through an angle sensor according to a fixed frequency, and calculating the mean value and standard deviation of the multiple angles; when the mean value is greater than a set switching threshold value, continuously monitoring two adjacent wires through a millimeter wave radar to obtain monitoring data; and correcting the monitoring data by adopting an improved Kalman filtering algorithm based on the standard deviation to obtain a conductor windage yaw monitoring result. The improved Kalman filtering algorithm is utilized to fuse the angle measurement value and the millimeter wave radar ranging result, stable and high-precision detection of the wire windage yaw characteristics is achieved, and then the problems that according to a windage yaw characteristic detection method in the prior art, errors are difficult to eliminate, the detection precision is limited, and the detection precision is low are solved. And effective images cannot be detected under the condition of low visibility.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance of transmission lines, and particularly to a method and system for monitoring conductor wind deflection by integrating angle and ranging information. Background Art

[0002] Monitoring the wind deflection of transmission line conductors plays a crucial role in the line's resistance to wind deflection disasters. Wind deflection is a phenomenon of conductor swing caused by wind. Due to the large degree of wind deflection, it can cause phase-to-phase flashover and damage to metal fixtures in minor cases, and serious accidents such as line tripping and power outage, pole tower collapse, and conductor breakage in severe cases, resulting in significant economic losses. It is one of the main disasters faced by overhead transmission lines. In recent years, relevant research focusing on online monitoring technology for transmission line wind deflection has made progress in many aspects, mainly concentrated in the following three methods:

[0003] (1) Inertial sensor monitoring method. Based on a triaxial accelerometer or an inertial measurement unit respectively, integral operations are performed on the acceleration data to obtain the angle and displacement. However, the calculations and analyses are mostly carried out through Matlab. The measurement accuracy of the accelerometer is insufficient, and the power consumption of the inertial measurement unit is relatively high, making it difficult to meet the long-term monitoring requirements;

[0004] (2) Wind deflection feature monitoring method based on video images. The identification of wind deflection feature parameters is realized through the captured video images. This method requires strict measurement of the installation angle and height of the camera on the tower, and the construction is relatively cumbersome;

[0005] (3) Method for measuring the absolute position of conductors based on the global positioning system. The fixed positioning module on the conductor is regarded as a rover station, and differential resolution is achieved through the tower or an existing reference station to obtain the position of the rover station. Then, the position of the conductor can be restored and visually displayed in the background. However, this method is easily restricted by the sampling frequency.

[0006] Looking at the above three methods comprehensively, the wind deflection feature measurement method based on inertial sensors is direct and simple to install, and can intuitively reflect the wind deflection state of the transmission line, but the error is difficult to eliminate; the wind deflection feature detection method based on video images is greatly affected by weather, climate, and light, and effective images cannot be detected in low visibility conditions; the method for measuring the absolute position of conductors based on the global positioning system, the detection accuracy is affected by the distance between the rover station and the reference station, and the detection accuracy is limited in mountainous or jungle areas where it is inconvenient to erect reference stations. Summary of the Invention

[0007] In order to solve the problems in the prior art such as the difficulty in eliminating errors in the wind deflection feature detection method, limited detection accuracy, and the inability to detect effective images in low visibility conditions, the present invention proposes a method for monitoring conductor wind deflection by integrating angle and ranging information, including:

[0008] Collect the angle multiple times at a fixed frequency through an angle sensor, and calculate the mean and standard deviation of the multiple angles;

[0009] When the mean is greater than the set switch threshold, continuously monitor the adjacent two wires through a millimeter-wave radar to obtain monitoring data;

[0010] Based on the standard deviation, use an improved Kalman filtering algorithm to correct the monitoring data to obtain the wire wind deflection monitoring result.

[0011] Optionally, the step of using an improved Kalman filtering algorithm based on the standard deviation to correct the monitoring data to obtain the wire wind deflection monitoring result includes:

[0012] Process the distance and angle between the two wires measured by the millimeter-wave radar to construct the covariance matrix of the state vector;

[0013] Replace the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new covariance matrix of the state vector;

[0014] Based on the covariance matrix of the new state vector and the set transformation matrix, obtain the improved Kalman gain coefficient;

[0015] Based on the corrected measurement result of the millimeter-wave radar at the previous moment and the process noise, combined with the pre-constructed prediction model, obtain the predicted value of the measurement result of the millimeter-wave radar at the current moment;

[0016] Calculate the measurement result of the millimeter-wave radar based on the set transformation matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment;

[0017] Based on the improved Kalman gain coefficient, the predicted value of the measurement result of the millimeter-wave radar at the current moment, and the measurement result of the millimeter-wave radar, obtain the corrected measurement result of the radar sensor.

[0018] Optionally, the prediction model is shown as the following formula:

[0019]

[0020] In the formula, is the prediction result of the millimeter-wave radar at time k, A is the state transition matrix, x′ k-1 is the corrected measurement result of the millimeter-wave radar at time k - 1, w k-1 is the process noise.

[0021] Optionally, the step of processing the distance and angle between the two wires measured by the millimeter-wave radar to construct the covariance matrix of the state vector includes:

[0022] Calculate the mean value of the distance between two wires measured by the millimeter-wave radar, and the mean value of the angle between the two wires measured by the millimeter-wave radar;

[0023] Based on the distance between the two wires, the angle between the two wires, the mean value of the distance between the two wires, and the mean value of the angle between the two wires, according to the definitions of variance and covariance, obtain the variance of the distance between the two wires, the variance of the angle, and the covariance;

[0024] Construct the covariance matrix of the state vector based on the variance of the distance between the two wires, the variance of the angle, and the covariance.

[0025] Optionally, the covariance matrix of the state vector is shown as follows:

[0026]

[0027] In the formula, P k is the covariance matrix of the state vector at time k, σ(R k ,R k ) is the variance of the distance between the two wires measured by the millimeter-wave radar, σ(θ k ,θ k ) is the variance of the angle between the two wires measured by the millimeter-wave radar, σ(θ k ,R k ) is the covariance of the angle and distance between the two wires measured by the millimeter-wave radar, σ(R k ,θ k ) is the covariance of the distance and angle between the two wires measured by the millimeter-wave radar, R k is the distance between the two wires measured by the millimeter-wave radar at time k, θ k is the angle between the two wires measured by the millimeter-wave radar at time k.

[0028] Optionally, the improved Kalman gain coefficient is calculated as follows:

[0029]

[0030] In the formula, K' k is the improved Kalman gain coefficient, P' k is the covariance matrix of the new state vector, H is the transformation matrix, and N is the covariance matrix of the radar measurement noise.

[0031] Optionally, the measurement result of the millimeter-wave radar is calculated as follows:

[0032] z k =Hx k +v k

[0033] where v k is the measurement noise of the millimeter-wave radar, z k is the measurement result of the millimeter-wave radar, H is the transformation matrix, and x k is the true measurement result of the millimeter-wave radar at time k.

[0034] Optionally, the measurement result of the calibrated radar sensor is calculated according to the following formula:

[0035]

[0036] where x' k is the measurement result of the calibrated radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

[0037] On the other hand, the present application also provides a wire wind deviation monitoring system that fuses angle and ranging information, including:

[0038] A calculation module, configured to collect angles multiple times at a fixed frequency through an angle sensor, and calculate the mean and standard deviation of the multiple angles;

[0039] A data acquisition module, configured to continuously monitor adjacent two wires through a millimeter-wave radar when the mean is greater than a set switch threshold to obtain monitoring data;

[0040] A corrected data module, configured to correct the monitoring data by using an improved Kalman filtering algorithm based on the standard deviation to obtain a wire wind deviation monitoring result.

[0041] Optionally, the corrected data module includes:

[0042] A matrix construction sub-module, configured to process the distance and angle between two wires measured by a millimeter-wave radar to construct a covariance matrix of the state vector;

[0043] An update sub-module, configured to replace the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new covariance matrix of the state vector;

[0044] A coefficient calculation sub-module, configured to obtain an improved Kalman gain coefficient based on the new covariance matrix of the state vector and a set transformation matrix;

[0045] The predictor sub-module is used to obtain the predicted value of the measurement result of the millimeter-wave radar at the current moment based on the corrected measurement result of the millimeter-wave radar at the previous moment and the process noise in combination with a pre-constructed prediction model;

[0046] The measured value calculation sub-module is used to calculate the measurement result of the millimeter-wave radar based on the set conversion matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment;

[0047] The correction sub-module is used to obtain the corrected measurement result of the radar sensor based on the improved Kalman gain coefficient, the predicted value of the measurement result of the millimeter-wave radar at the current moment, and the measurement result of the millimeter-wave radar.

[0048] Optionally, the matrix construction sub-module is specifically used for:

[0049] Calculating the mean value of the distance between two wires measured by the millimeter-wave radar and the mean value of the angle between the two wires measured by the millimeter-wave radar;

[0050] Based on the distance between the two wires, the angle between the two wires, the mean value of the distance between the two wires, and the mean value of the angle between the two wires, according to the definitions of variance and covariance, obtain the variance of the distance between the two wires, the variance of the angle, and the covariance;

[0051] Construct the covariance matrix of the state vector based on the variance of the distance between the two wires, the variance of the angle, and the covariance.

[0052] Optionally, the coefficient calculation sub-module calculates the improved Kalman gain coefficient according to the following formula:

[0053]

[0054] In the formula, K' k is the improved Kalman gain coefficient, P' k is the new covariance matrix of the state vector, H is the conversion matrix, and N is the covariance matrix of the radar measurement noise.

[0055] Optionally, the noise calculation sub-module calculates the measurement result of the millimeter-wave radar according to the following formula:

[0056] z k = Hx k + v k

[0057] In the formula, v k is the measurement noise of the millimeter-wave radar, z k is the measurement result of the millimeter-wave radar, H is the conversion matrix, and x k is the true measurement result of the millimeter-wave radar at time k.

[0058] Optionally, the syndrome sub-module calculates the measurement result of the calibrated radar sensor using the following formula:

[0059]

[0060] where x' k is the measurement result of the calibrated radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

[0061] The prediction model in the prediction sub-module is shown by the following formula:

[0062]

[0063] where is the prediction result of the millimeter-wave radar at time k, A is the state transition matrix, x′ k-1 is the measurement result of the millimeter-wave radar calibrated at time k-1, w k-1 is the process noise.

[0064] On the other hand, the present application also provides a computing device, including: one or more processors;

[0065] The processor is configured to execute one or more programs;

[0066] When the one or more programs are executed by the one or more processors, a wire wind deflection monitoring method for fusing angle and ranging information as described above is implemented.

[0067] On the other hand, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, a wire wind deflection monitoring method for fusing angle and ranging information as described above is implemented.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] The present invention provides a wire wind deflection monitoring method that integrates angle and ranging information, including: collecting angles multiple times at a fixed frequency through an angle sensor, and calculating the mean and standard deviation of the multiple angles; when the mean is greater than a set switching threshold, continuously monitoring two adjacent wires through a millimeter-wave radar to obtain monitoring data; and correcting the monitoring data based on the standard deviation using an improved Kalman filtering algorithm to obtain the wire wind deflection monitoring result. This application uses the improved Kalman filtering algorithm to fuse the angle measurement values and the ranging results of the millimeter-wave radar, achieving stable and high-precision detection of wire wind deflection characteristics, and further solving the problems in the prior art such as the difficulty in eliminating errors in the wind deflection characteristic detection method, limited detection accuracy, and the inability to detect effective images in low visibility conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flowchart of a wire wind deflection monitoring method that integrates angle and ranging information according to the present invention;

[0071] Figure 2 It is a schematic diagram of the phase wire spacing detection of the millimeter-wave radar according to the present invention;

[0072] Figure 3 It is a schematic diagram of the movement trajectory of the angle sensor on the wire when the wire is moving according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] Based on the millimeter-wave radar and multi-sensor fusion power transmission line wind deflection attitude monitoring method of the present invention, an improved Kalman filtering algorithm is used to fuse the angle measurement values and the ranging results of the low-power millimeter-wave radar, achieving stable and high-precision detection of wire wind deflection characteristics. And based on the standardized low-power wireless communication technology for power transmission scenarios, stable data transmission is realized, avoiding the disadvantages of many limited factors and large detection result errors in traditional indirect measurements, and realizing intelligent and unmanned monitoring and early warning.

[0074] Embodiment 1:

[0075] A wire wind deflection monitoring method that integrates angle and ranging information, as Figure 1 shown, includes:

[0076] Step 1: Collect angles multiple times at a fixed frequency through an angle sensor, and calculate the mean and standard deviation of the multiple angles;

[0077] Step 2: When the mean is greater than a set switching threshold, continuously monitor two adjacent wires through a millimeter-wave radar to obtain monitoring data;

[0078] Step 3: Correct the monitoring data based on the standard deviation using an improved Kalman filtering algorithm to obtain the wire wind deflection monitoring result.

[0079] Step 1: Collect the angles multiple times at a fixed frequency through an angle sensor, and calculate the mean and standard deviation of the multiple angles, specifically including:

[0080] The intuitive measurement parameter for wind deflection monitoring is the distance between two adjacent conductors. Considering the requirement of low power consumption, an angle sensor is first used to measure the wind deflection angle, and when the wind deflection angle reaches a certain value, the radar switch is triggered. The millimeter-wave radar is built into the wind deflection sensor and emits electromagnetic waves towards adjacent-phase conductors to measure the distance between two adjacent conductors. Since the device is installed on the conductor, the device will vibrate in windy conditions, so the millimeter-wave radar data detected will introduce noise.

[0081] To solve the problem of unstable data, an optimization algorithm is adopted. The mean and standard deviation of the statistical parameters of the wind deflection angle measured by the angle sensor are used as calibration parameters for the millimeter-wave radar to optimize the stability and accuracy of the millimeter-wave radar measurement data. The output data is transmitted to the edge intelligent gateway through the IEEE802.15.4 protocol, and the monitoring principle is as follows Figure 2 as shown.

[0082] Fix the device reliably on the conductor in the manner of Figure 2 . When the conductor is stationary, the angle sensor is located at point O. When the conductor moves, the device moves along with the conductor according to the trajectory shown in Figure 3 . When the conductor moves to point A', the angle sensor measures the wind deflection angle α at this time; the angle sensor collects the angle α at the same frequency as the radar k , k = 1, 2, 3,......, n, representing the wind deflection angle collected at the k-th moment, and calculate its mean E(α k ) and standard deviation δ k ;

[0083] Step 2: When the mean is greater than the set switch threshold, continuously monitor the two adjacent conductors through the millimeter-wave radar to obtain monitoring data, specifically including:

[0084] Set the switch threshold α0 of the millimeter-wave radar. When E(α) < α0, the conductor is in a windless state and is safe; when E(α) > α0, turn on the millimeter-wave radar, and the millimeter-wave radar starts to continuously detect the distance between the two adjacent conductors;

[0085] The millimeter-wave radar continuously detects the distance between the two adjacent conductors at a fixed frequency, and the output value is where R k is the distance between the two conductors directly measured by the millimeter-wave radar at the k-th moment, and θ k is the angle between the two conductors measured by the millimeter-wave radar at the k-th moment;

[0086] Under on-site conditions, the influence of meteorological factors such as wind will cause vibrations in the detection equipment, so noise v is added to the detection results of the millimeter-wave radar. k , noise v k follows a Gaussian distribution; in order to remove the influence of vibrations on the detection results, an improved Kalman filtering algorithm is used to optimize the data of the millimeter-wave radar. The processing of the millimeter-wave radar data by this algorithm is as follows:

[0087] Step 3: Based on the standard deviation, use the improved Kalman filtering algorithm to correct the monitoring data to obtain the monitoring result of the conductor wind deflection, specifically including:

[0088] Process the distance and angle between the two conductors measured by the millimeter-wave radar to construct the covariance matrix of the state vector;

[0089] Replace the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new covariance matrix of the state vector;

[0090] Based on the covariance matrix of the new state vector and the set transformation matrix, obtain the improved Kalman gain coefficient;

[0091] Based on the corrected measurement result of the millimeter-wave radar at the previous moment and the process noise, combined with the pre-constructed prediction model, obtain the predicted value of the measurement result of the millimeter-wave radar at the current moment;

[0092] Based on the set transformation matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment, calculate the measurement result of the millimeter-wave radar;

[0093] Based on the improved Kalman gain coefficient, the predicted value of the measurement result of the millimeter-wave radar at the current moment, and the measurement result of the millimeter-wave radar, obtain the corrected measurement result of the radar sensor.

[0094] Furthermore, processing the distance and angle between the two conductors measured by the millimeter-wave radar to construct the covariance matrix of the state vector includes:

[0095] Calculate the mean value of the distance between the two conductors measured by the millimeter-wave radar and the mean value of the angle between the two conductors measured by the millimeter-wave radar;

[0096] Based on the distance between the two conductors, the angle between the two conductors, the mean value of the distance between the two conductors, and the mean value of the angle between the two conductors, according to the definitions of variance and covariance, obtain the variance, angle variance, and covariance of the distance between the two conductors;

[0097] Based on the variance of the distance between the two conductors, the angle variance, and the covariance, construct the covariance matrix of the state vector.

[0098] Further, based on the distance between the two wires, the angle between the two wires, the mean value of the distance between the two wires, and the mean value of the angle between the two wires, according to the definitions of variance and covariance, the variance, angle variance, and covariance of the distance between the two wires are obtained, specifically including:

[0099] In statistics, variance is used to measure the degree of dispersion of a single random variable, while covariance is generally used to describe the similarity degree of two random variables. Among them, the calculation formula of variance is:

[0100]

[0101] where n represents the sample size, represents the mean value of the observed samples, x k is the sample observation value of the random variable x at time k, k = 1, 2, 3,..., n, and σ 2 represents variance.

[0102] On this basis, the calculation formula of covariance is defined as:

[0103]

[0104] In the formula, the symbols respectively represent the mean values of the observed samples corresponding to the two random variables. Based on this, we find that variance can be regarded as the covariance σ(x, x) of the random variable x with respect to itself, σ(x, y) is the covariance of the random variables x and y, x k is the sample observation value of the random variable x at time k, y k is the observation value of the random variable y at time k.

[0105] According to the definition of variance and combined with this system, the two given random variables are R and θ. Then the variances of these random variables are:

[0106]

[0107]

[0108] where, and are respectively the means of R k , θ k (k = 1, 2,..., n), σ(R k , R k ) is the variance of the distance between the two wires measured by the millimeter-wave radar, and σ(θ k , θ k) is the variance of the angle between two wires measured by the millimeter-wave radar, R k is the distance between two wires measured by the millimeter-wave radar at time k, θ k is the angle between two wires measured by the millimeter-wave radar at time k, and n represents the sample size.

[0109] For these two random variables, according to the definition of covariance, their covariance is:

[0110]

[0111] Therefore, the covariance matrix of the state vector is:

[0112]

[0113] In the formula, P k is the covariance matrix of the state vector at time k, σ(R k , R k ) is the variance of the distance between two wires measured by the millimeter-wave radar, σ(θ k , θ k ) is the variance of the angle between two wires measured by the millimeter-wave radar, σ(θ k , R k ) is the covariance of the angle and distance between two wires measured by the millimeter-wave radar, σ(R k , θ k ) is the covariance of the distance and angle between two wires measured by the millimeter-wave radar, R k is the distance between two wires measured by the millimeter-wave radar at time k, θ k is the angle between two wires measured by the millimeter-wave radar at time k.

[0114] Furthermore, based on the covariance matrix of the new state vector and the set transformation matrix, an improved Kalman gain coefficient is obtained, including:

[0115] Solve the Kalman gain coefficient K k :

[0116] P k = AP k-1 A T + Q k (1.7)

[0117] Among them, P k is the covariance matrix of the state vector at time k, representing the relationship between each element of the state vector; Q k represents the covariance matrix of Gaussian noise, and P k-1 is the covariance matrix of the state vector at time k - 1.

[0118] Kalman gain coefficient K k is:

[0119]

[0120] where N is the covariance matrix of radar measurement noise, and H is the transformation matrix; in P k , σ(θ k , θ k ) is the variance of the radar measurement angle, representing the dispersion degree of the measurement angle caused by the jitter of the radar; and the variance of the measurement result of the angle sensor has the same mathematical meaning, and the causes of both are due to the jitter of the sensor caused by the wind during the measurement process. Therefore, the value of σ(θ k in P k , θ k ) is replaced through to obtain the new state vector covariance matrix P' k . Therefore, there is:

[0121]

[0122] In the formula, K' k is the improved Kalman gain coefficient, P' k is the new state vector covariance matrix, H is the transformation matrix, and N is the covariance matrix of radar measurement noise.

[0123] This application also includes: establishing a prediction model:

[0124]

[0125] where is the predicted value of the measurement result of the millimeter-wave radar at time k, x′ k-1 is the corrected measurement result of the millimeter-wave radar at time k - 1, is the predicted value of the distance between two wires measured by the millimeter-wave radar at time k, and θ k is the predicted value of the angle between two wires measured by the millimeter-wave radar at time k; A is the state transition matrix, representing the transfer of the state vector at time k - 1 to the state vector at time k; u k-1 is the control vector; B is the input control matrix; w k-1 is the process noise. In this model, no additional control is introduced, so Therefore, there is:

[0126]

[0127] In the formula, The prediction result of the millimeter-wave radar at time k, A is the state transition matrix, x' k-1 is the corrected measurement result of the millimeter-wave radar at time k - 1, w k-1 is the process noise.

[0128] Furthermore, based on the corrected measurement result of the millimeter-wave radar at the previous time and the process noise, combined with a pre-constructed prediction model, the predicted value of the measurement result of the millimeter-wave radar at the current time is obtained, including:

[0129] Substitute the predicted value of the measurement result of the millimeter-wave radar at the previous time and the process noise into the prediction model to obtain the predicted value of the measurement result of the millimeter-wave radar at the current time. The prediction model is as follows:

[0130]

[0131] In the formula, is the prediction result of the millimeter-wave radar at time k, A is the state transition matrix, x' k-1 is the corrected measurement result of the millimeter-wave radar at time k - 1, w k-1 is the process noise.

[0132] Furthermore, based on the set conversion matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current time, calculate the measurement result of the millimeter-wave radar, specifically including:

[0133] Substitute the set conversion matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current time into the following calculation formula to obtain the measurement result of the millimeter-wave radar:

[0134] z k = Hx k + v k (1.12)

[0135] where z k is the measurement result of the millimeter-wave radar; x k is the true measurement result of the millimeter-wave radar at time k, R k is the distance between the two wires measured by the millimeter-wave radar at time k, θ k is the angle between the two wires measured by the millimeter-wave radar at time k; v k is the measurement noise of the radar, that is, the noise introduced by the vibration of the radar during the process of measuring radar data; H is the conversion matrix, and this conversion matrix H can be set according to the system situation to map x k to the vector space z k .

[0136] Further, based on the improved Kalman gain coefficient, the measurement noise of the millimeter-wave radar, and the true measurement result of the millimeter-wave radar, the measurement result of the calibrated radar sensor is obtained, specifically including:

[0137] The measurement result of the calibrated radar sensor is:

[0138]

[0139] In the formula, x′ k is the measurement result of the calibrated radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

[0140]

[0141] Among them, x' k is the measurement result of the calibrated radar sensor, R' k is the distance between the two wires measured by the calibrated millimeter-wave radar at time k, θ' k is the angle between the two wires measured by the calibrated millimeter-wave radar at time k.

[0142] The measurement results of the calibrated millimeter-wave radar at time k (k = 1, 2, 3,......, n): the wire wind deflection value R’ k and the wind deflection angle θ’ k are transmitted to the edge intelligent gateway through the communication method of IEEE802.15.4, completing the autonomous calculation at the sensor front end and greatly reducing the data transmission volume.

[0143] Based on the ranging information and angle monitoring information of the millimeter-wave radar, the present invention uses an improved Kalman filtering algorithm to fuse the angle measurement value and the ranging result of the low-power millimeter-wave radar, realizing stable and high-precision detection of the wire wind deflection characteristics. All calculation processes are completed in the sensor, greatly reducing the radar and angle data transmission volume, realizing the adaptation of the standardized low-power wireless communication technology for the power transmission scenario, and at the same time avoiding the disadvantages of many limited factors and large detection result errors in traditional indirect measurements.

[0144] Reliably fix the device containing the millimeter-wave radar and angle monitoring integrated sensor used in the present invention on the wire of the transmission line, and install the edge intelligent gateway near the pole tower. The device and the edge intelligent gateway communicate through the IEEE802.15.4 short-range wireless communication method. Measure the wind deflection angle and wind deflection value of the wire through the angle sensor and millimeter-wave radar inside the device. Use the improved Kalman filtering algorithm in the sensor, and directly obtain the characteristic value of the minimum phase distance of wind deflection monitoring after correcting the radar measurement result with the standard deviation of the angle, and send it to the edge intelligent gateway to complete the intelligent monitoring of the wire wind deflection state on the edge side.

[0145] Embodiment 2:

[0146] Based on the same inventive concept, the present invention also provides a wire wind deflection monitoring system integrating angle and ranging information, including:

[0147] A calculation module, configured to collect angles multiple times at a fixed frequency through an angle sensor, and calculate the mean and standard deviation of the multiple angles;

[0148] A data acquisition module, configured to continuously monitor adjacent two wires through a millimeter-wave radar when the mean value is greater than a set switch threshold, and obtain monitoring data;

[0149] A corrected data module, configured to correct the monitoring data by using an improved Kalman filtering algorithm based on the standard deviation to obtain a wire wind deflection monitoring result.

[0150] Further, the corrected data module includes:

[0151] A matrix construction sub-module, configured to process the distance and angle between two wires measured by a millimeter-wave radar, and construct a covariance matrix of the state vector;

[0152] An update sub-module, configured to replace the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new state vector covariance matrix;

[0153] A coefficient calculation sub-module, configured to obtain an improved Kalman gain coefficient based on the new state vector covariance matrix and a set transformation matrix;

[0154] A prediction sub-module, configured to obtain a predicted value of the measurement result of the millimeter-wave radar at the current moment based on the corrected measurement result of the millimeter-wave radar at the previous moment, process noise, and a pre-constructed prediction model;

[0155] A measured value calculation sub-module, configured to calculate the measurement result of the millimeter-wave radar based on a set transformation matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment;

[0156] The corrector module is used to obtain the corrected measurement result of the radar sensor based on the improved Kalman gain coefficient, the predicted value measured by the millimeter-wave radar at the current moment, and the true measurement result of the millimeter-wave radar.

[0157] Further, the matrix construction sub-module is specifically used for:

[0158] Calculating the mean value of the distance between two wires measured by the millimeter-wave radar, and the mean value of the angle between two wires measured by the millimeter-wave radar;

[0159] Based on the distance between the two wires, the angle between the two wires, the mean value of the distance between the two wires, and the mean value of the angle between the two wires, according to the definitions of variance and covariance, obtaining the variance, angle variance, and covariance of the distance between the two wires;

[0160] Constructing the covariance matrix of the state vector based on the variance of the distance between the two wires, the angle variance, and the covariance.

[0161] Further, the coefficient calculation sub-module calculates the improved Kalman gain coefficient according to the following formula:

[0162]

[0163] In the formula, K' k is the improved Kalman gain coefficient, P' k is the new state vector covariance matrix, H is the transformation matrix, and N is the covariance matrix of the radar measurement noise.

[0164] Further, the noise calculation sub-module calculates the measurement noise of the millimeter-wave radar according to the following formula:

[0165] z k =Hx k +v k

[0166] In the formula, v k is the measurement noise of the millimeter-wave radar, z k is the measurement result of the millimeter-wave radar, H is the transformation matrix, and x k is the true measurement result of the millimeter-wave radar at time k.

[0167] Further, the corrector module calculates the corrected measurement result of the radar sensor according to the following formula:

[0168]

[0169] In the formula, x' k is the corrected measurement result of the radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

[0170] Furthermore, the prediction model is shown as follows:

[0171]

[0172] In the formula, is the prediction result of the millimeter-wave radar at time k, A is the state transition matrix, x′ k-1 is the measurement result of the millimeter-wave radar after calibration at time k - 1, w k-1 is the process noise.

[0173] Embodiment 3:

[0174] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for monitoring the wind deflection of a wire by integrating angle and ranging information in the above embodiments.

[0175] Embodiment 4:

[0176] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, there is also stored one or more instructions suitable for being loaded and executed by the processor, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of the method for monitoring conductor wind deflection by integrating angle and ranging information in the above embodiments.

[0177] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0178] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0179] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and this instruction device implements the functions in Figure 1 one flow or multiple flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one Figure 1 one process or more processes and / or boxes Figure 1 or more boxes.

[0181] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A wire wind deflection monitoring method that fuses angle and ranging information, characterized in that Including: Collecting angles multiple times at a fixed frequency through an angle sensor, and calculating the mean and standard deviation of the multiple angles; When the mean is greater than a set switch threshold, continuously monitoring two adjacent wires through a millimeter-wave radar to obtain monitoring data; Based on the standard deviation, using an improved Kalman filtering algorithm to correct the monitoring data to obtain a wire wind deflection monitoring result.

2. The method according to claim 1, characterized in that, The step of using an improved Kalman filtering algorithm based on the standard deviation to correct the monitoring data to obtain a wire wind deflection monitoring result includes: Processing the distance and angle between the two wires measured by the millimeter-wave radar to construct a covariance matrix of the state vector; Replacing the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new covariance matrix of the state vector; Based on the new covariance matrix of the state vector and a set transformation matrix, obtaining an improved Kalman gain coefficient; Based on the corrected measurement result of the millimeter-wave radar at the previous moment and the process noise, combining with a pre-constructed prediction model, obtaining a predicted value of the measurement result of the millimeter-wave radar at the current moment; Calculating the measurement result of the millimeter-wave radar based on a set transformation matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment; Based on the improved Kalman gain coefficient, the predicted value of the measurement result of the millimeter-wave radar at the current moment, and the measurement result of the millimeter-wave radar, obtaining the corrected measurement result of the radar sensor.

3. The method according to claim 2, wherein The prediction model is shown as the following formula: In the formula, is the prediction result of the millimeter-wave radar at time k, A is the state transition matrix, and x′ k-1 is the measurement result of the millimeter-wave radar at time k-1 after calibration, and w k-1 is the process noise.

4. The method according to claim 2, wherein The step of processing the distance and angle between the two wires measured by the millimeter-wave radar to construct a covariance matrix of the state vector includes: Calculating the mean of the distance between the two wires measured by the millimeter-wave radar and the mean of the angle between the two wires measured by the millimeter-wave radar; Based on the distance between the two wires, the angle between the two wires, the mean of the distance between the two wires, and the mean of the angle between the two wires, according to the definitions of variance and covariance, obtaining the variance of the distance between the two wires, the variance of the angle, and the covariance; Based on the variance of the distance between the two wires, the variance of the angle, and the covariance, constructing a covariance matrix of the state vector.

5. The method according to claim 2, characterized in that, The covariance matrix of the state vector is shown as the following formula: where P k is the covariance matrix of the state vector at time k, σ(R k , R k ) is the variance of the distance between two wires measured by the millimeter-wave radar, σ(θ k , θ k ) is the variance of the angle between two wires measured by the millimeter-wave radar, σ(R k , θ k ) is the covariance of the distance and angle between two wires measured by the millimeter-wave radar; σ(θ k , R k ) is the covariance of the angle and distance between two wires measured by the millimeter-wave radar, R k is the distance between two wires measured by the millimeter-wave radar at time k, and θ k is the angle between two wires measured by the millimeter-wave radar at time k.

6. The method according to claim 2, characterized in that The improved Kalman gain coefficient is calculated according to the following formula: where K' k is the improved Kalman gain coefficient, P k ' is the new state vector covariance matrix, H is the transformation matrix, and N is the covariance matrix of the radar measurement noise.

7. The method according to claim 2, characterized in that, The measurement result of the millimeter-wave radar is calculated according to the following formula: z k = Hx k + v k where, v k is the measurement noise of the millimeter-wave radar, z k is the measurement result of the millimeter-wave radar, H is the conversion matrix, and x k is the true measurement result of the millimeter-wave radar at time k.

8. The method according to claim 2, wherein The corrected measurement result of the radar sensor is calculated according to the following formula: where x' k is the measurement result of the calibrated radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

9. A conductor wind deflection monitoring system that integrates angle and ranging information, characterized in that, Including: A calculation module for collecting angles multiple times at a fixed frequency through an angle sensor and calculating the mean and standard deviation of the multiple angles; A data acquisition module for continuously monitoring two adjacent wires through a millimeter-wave radar to obtain monitoring data when the mean is greater than a set switch threshold; A data correction module for correcting the monitoring data based on the standard deviation using an improved Kalman filtering algorithm to obtain a wire wind deflection monitoring result.

10. The system according to claim 9, characterized in that, The data correction module includes: A matrix construction sub-module for processing the distance and angle between the two wires measured by the millimeter-wave radar to construct a covariance matrix of the state vector; An update sub-module, configured to replace the values in the covariance matrix of the state vector with the square of the standard deviation to obtain a new state vector covariance matrix; A coefficient calculation sub-module, configured to obtain an improved Kalman gain coefficient based on the covariance matrix of the new state vector and a set transformation matrix; A prediction sub-module, configured to obtain a predicted value of the measurement result of the millimeter-wave radar at the current moment based on the corrected measurement result of the millimeter-wave radar at the previous moment, process noise, and a pre-constructed prediction model; A measurement value calculation sub-module, configured to calculate the measurement result of the millimeter-wave radar based on a set transformation matrix, the measurement noise of the radar, and the true measurement result of the millimeter-wave radar at the current moment; A correction sub-module, configured to obtain the corrected measurement result of the radar sensor based on the improved Kalman gain coefficient, the predicted value of the measurement result of the millimeter-wave radar at the current moment, and the measurement result of the millimeter-wave radar; 11. The system according to claim 10, wherein, The matrix construction sub-module is specifically configured to: Calculate the mean value of the distance between two wires measured by the millimeter-wave radar and the mean value of the angle between the two wires measured by the millimeter-wave radar; Based on the distance between the two wires, the angle between the two wires, the mean value of the distance between the two wires, and the mean value of the angle between the two wires, obtain the variance of the distance between the two wires, the variance of the angle, and the covariance according to the definitions of variance and covariance; Construct a covariance matrix of the state vector based on the variance of the distance between the two wires, the variance of the angle, and the covariance; 12. The system according to claim 10, wherein, The coefficient calculation sub-module calculates the improved Kalman gain coefficient according to the following formula: where K' k is the improved Kalman gain coefficient, P′ k is the covariance matrix of the state vector at the new k-th moment, H is the transformation matrix, and N is the covariance matrix of the radar measurement noise.

13. The system according to claim 10, wherein The correction sub-module calculates the corrected measurement result of the radar sensor according to the following formula: where x' k is the measurement result of the calibrated radar sensor, is the prediction result of the millimeter-wave radar at time k, K' k is the improved Kalman gain coefficient, z k is the measurement result of the millimeter-wave radar, and H is the transformation matrix.

14. The system according to claim 10, wherein The measurement value calculation sub-module calculates the measurement result of the millimeter-wave radar according to the following formula: z k = Hx k + v k where, v k is the measurement noise of the millimeter-wave radar, z k is the measurement result of the millimeter-wave radar, H is the transformation matrix, x k is the true measurement result of the millimeter-wave radar at time k.

15. A computer device, characterized in that, Including: One or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, implement a wire wind deviation monitoring method for fusing angle and ranging information as described in any one of claims 1 to 8.

16. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed, implement a wire wind deviation monitoring method for fusing angle and ranging information as described in any one of claims 1 to 8.

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